Machine Learning  

• Foundations

  • Shannon's Source Coding Theorem

  • Bayes Rule

  • Cox Axioms

  • Bayesian model comparison

• Models

  • Factor Analysis / PCA

  • Independent Components Analysis (ICA)

  • Mixture models / k-means

  • Hidden Markov models (HMMs)

  • State space models (SSMs)

  • Boltzmann machines

  • Graphical models: directed, undirected, factor graphs

• Algorithms

  • The EM Algorithm

  • Belief propagation

  • Forward-backward

  • Kalman filtering and extended Kalman filtering

  • Variational methods

  • Laplace approximation and BIC

  • Markov chain Monte Carlo (MCMC) methods

  • Particle filters

  • Expectation propagation

• Supervised Learning:

  • Linear regression

  • Logistic regression

  • Perceptrons

  • Neural networks (multi-layer perceptrons) and backpropagation

  • Gaussian processes

  • Support vector machines

• Reinforcement Learning

  • Value functions

  • Bellman's equation

  • Value iteration

  • Policy iteration

  • Q-Learning

  • actor-critic

  • TD(lambda)

• Basic Learning Theory

  • VC dimension

  • regularization